Reproducibility of Digital Research Projects

Jonas Kreutzer
pjkreutzer.github.io

2025-07-28

What to expect!

  1. stop losing orientation in your projects
  2. consistent execution of code
  3. FAIR data
  4. (mostly) tool agnostic, but opinionated

01. Project Set Up

.
├── analysis
├── data
│   ├── interim
│   ├── processed
│   └── raw
├── docs
└── results

Tools that help:

Cookiecutter use templates or create your own, for example:

ccds

R devtools similar for R

Use Good File Names

👩‍💻 Human readable use descriptive names

🤖 Machine readable use slug naming with regex in mind

📥 Play well with default ordering

start at 01, use YYYYMMDD

File Names Examples

😭

old data.csv

data_v2thisisthemostimportantworkingversinofourdataneverdelete _update second author.xslx

figure.png

🥰

old_data.csv

20230929-raw_data.xslx

Fig01-scatterplot_happy.png

02. Project Execution

Analysis as a DAG

Figure 1: Schematic of a research project. Image from The Turing Way Community (2025)

Tools that help:

Quarto an open-source scientific and technical publishing system

Make a build automation tool

Treat Your Data Like the Treasure it Is

Make it FAIR

Findability,

Accessibility,

Interoperability and

Reusability of digital assets

Raw data is READ ONLY

Treat any output as disposable

Separate functionality from execution

Document what you do

Reproducible Environments

“I don’t know. It worked on my machine 🤷”

💻 ➡️ 📦

Tools that help:

Package Management Containers
pixi docker
conda
uv

03. Share your Project

  1. Decide what to share
  2. Choose a data repository
  3. Choose a license
  4. Upload data, metadata and documentation

Tools that help:

FAIRsharing.org database registry

Zenodo

Harvard Dataverse

choosealicense.com

github gitlab

Resources

Much of the content discussed in this talk can be found in great detail at The Turing Way – Guide for Project Design.

readme.so for quickly creating README.md.

Patrick Mineault’s Good Research Code Handbook is a great resource for more computational focused workflows, so is Nice R Code.

Get this presentation

bit.ly/pjk_dig_proj

Help, I am overwhelmed! This is too much. My collaborators will never. . .

  1. Start small (I suggest with project structure)

  2. Improve one file name at a time

  3. Set and communicate expectations clearly

  4. Fix small mistakes early

👋 (pjkreutzer?)

References

The Turing Way Community. 2025. “The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Research.” Zenodo. https://doi.org/10.5281/zenodo.15213042.